Remove Data Lake Remove Data Processing Remove Data Warehouse Remove Enterprise
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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 109
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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud. Learn from this to build querying capabilities across your data lake and the data warehouse. Let’s find out what role each of these components play in the context of C360.

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

Typically, you have multiple accounts to manage and run resources for your data pipeline. He has a track record of more than 18 years innovating and delivering enterprise products that unlock the power of data for users. Outside of work, Xiaorun enjoys exploring new places in the Bay Area.

Metrics 101